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BackTo2014
/
mnist-demo2

Unconditional Image Generation
Diffusers
Safetensors
PyTorch
DDPMPipeline
diffusion-models-class
Model card Files Files and versions
xet
Community

Instructions to use BackTo2014/mnist-demo2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use BackTo2014/mnist-demo2 with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import DiffusionPipeline
    
    # switch to "mps" for apple devices
    pipe = DiffusionPipeline.from_pretrained("BackTo2014/mnist-demo2", dtype=torch.bfloat16, device_map="cuda")
    
    prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
    image = pipe(prompt).images[0]
  • Notebooks
  • Google Colab
  • Kaggle
mnist-demo2
74.2 MB
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  • 1 contributor
History: 5 commits
BackTo2014's picture
BackTo2014
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